1
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Robin X, Studer G, Durairaj J, Eberhardt J, Schwede T, Walters WP. Assessment of protein-ligand complexes in CASP15. Proteins 2023; 91:1811-1821. [PMID: 37795762 DOI: 10.1002/prot.26601] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023]
Abstract
CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.
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Affiliation(s)
- Xavier Robin
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Gabriel Studer
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Janani Durairaj
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jerome Eberhardt
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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2
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Leemann M, Sagasta A, Eberhardt J, Schwede T, Robin X, Durairaj J. Automated benchmarking of combined protein structure and ligand conformation prediction. Proteins 2023; 91:1912-1924. [PMID: 37885318 DOI: 10.1002/prot.26605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 09/15/2023] [Accepted: 09/21/2023] [Indexed: 10/28/2023]
Abstract
The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.
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Affiliation(s)
- Michèle Leemann
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Ander Sagasta
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jerome Eberhardt
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Xavier Robin
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Janani Durairaj
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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3
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Kryshtafovych A, Antczak M, Szachniuk M, Zok T, Kretsch RC, Rangan R, Pham P, Das R, Robin X, Studer G, Durairaj J, Eberhardt J, Sweeney A, Topf M, Schwede T, Fidelis K, Moult J. New prediction categories in CASP15. Proteins 2023; 91:1550-1557. [PMID: 37306011 PMCID: PMC10713864 DOI: 10.1002/prot.26515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 06/13/2023]
Abstract
Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.
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Affiliation(s)
| | - Maciej Antczak
- Institute of Computing Science, Poznan University of TechnologyPoznanPoland
- Institute of Bioorganic Chemistry, Polish Academy of SciencesPoznanPoland
| | - Marta Szachniuk
- Institute of Computing Science, Poznan University of TechnologyPoznanPoland
- Institute of Bioorganic Chemistry, Polish Academy of SciencesPoznanPoland
| | - Tomasz Zok
- Institute of Computing Science, Poznan University of TechnologyPoznanPoland
- Institute of Bioorganic Chemistry, Polish Academy of SciencesPoznanPoland
| | - Rachael C. Kretsch
- Biophysics Program, Stanford University School of MedicineStanfordCaliforniaUSA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of MedicineStanfordCaliforniaUSA
| | - Phillip Pham
- Biochemistry DepartmentStanford University School of MedicineStanfordCaliforniaUSA
| | - Rhiju Das
- Biochemistry DepartmentStanford University School of MedicineStanfordCaliforniaUSA
- Howard Hughes Medical Institute, Stanford UniversityStanfordCaliforniaUSA
| | - Xavier Robin
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | - Gabriel Studer
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | - Janani Durairaj
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | - Jerome Eberhardt
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | - Aaron Sweeney
- Centre for Structural Systems Biology (CSSB), Leibniz‐Institut für Virologie (LIV)HamburgGermany
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz‐Institut für Virologie (LIV)HamburgGermany
- Universitätsklinikum Hamburg Eppendorf (UKE)HamburgGermany
| | - Torsten Schwede
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | | | - John Moult
- Institute for Bioscience and Biotechnology Research, Department of Cell Biology and Molecular genetics, University of MarylandRockvilleMarylandUSA
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4
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CACHE (Critical Assessment of Computational Hit-finding Experiments): A public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding. Nat Rev Chem 2022; 6:287-295. [PMID: 35783295 PMCID: PMC9246350 DOI: 10.1038/s41570-022-00363-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.
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5
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Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. J Cheminform 2020; 12:9. [PMID: 33430992 PMCID: PMC6988305 DOI: 10.1186/s13321-020-0408-x] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 01/02/2020] [Indexed: 12/11/2022] Open
Abstract
The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.
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Affiliation(s)
- Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Saw Simeon
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 10900, Bangkok, Thailand
| | - Matthew Paul Gleeson
- Department of Biomedical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 10520, Bangkok, Thailand.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden.
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand.
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6
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da Silva Rocha SF, Olanda CG, Fokoue HH, Sant'Anna CM. Virtual Screening Techniques in Drug Discovery: Review and Recent Applications. Curr Top Med Chem 2019; 19:1751-1767. [DOI: 10.2174/1568026619666190816101948] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 06/21/2019] [Accepted: 07/29/2019] [Indexed: 11/22/2022]
Abstract
The discovery of bioactive molecules is an expensive and time-consuming process and new
strategies are continuously searched for in order to optimize this process. Virtual Screening (VS) is one
of the recent strategies that has been explored for the identification of candidate bioactive molecules.
The number of new techniques and software that can be applied in this strategy has grown considerably
in recent years, so, before their use, it is necessary to understand the basics an also the limitations behind
each one to get the most out of them. It is also necessary to assess the real contributions of this strategy
so that more significant progress can be made in the future. In this context, this review aims to discuss
some important points related to VS, including the use of virtual ligand and biotarget libraries, structurebased
and ligand-based VS techniques, as well as to present recent cases where this strategy was successfully
applied.
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Affiliation(s)
- Sheisi F.L. da Silva Rocha
- Programa de Pos-Graduacao em Quimica, Instituto de Quimica, Universidade Federal Rural do Rio de Janeiro, Seropedica, Brazil
| | - Carolina G. Olanda
- Programa de Pos-Graduacao em Quimica, Instituto de Quimica, Universidade Federal Rural do Rio de Janeiro, Seropedica, Brazil
| | - Harold H. Fokoue
- Laboratorio de Avaliacao e Síntese de Substancias Bioativas (LASSBio), Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Carlos M.R. Sant'Anna
- Programa de Pos-Graduacao em Quimica, Instituto de Quimica, Universidade Federal Rural do Rio de Janeiro, Seropedica, Brazil
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7
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Wagner JR, Churas CP, Liu S, Swift RV, Chiu M, Shao C, Feher VA, Burley SK, Gilson MK, Amaro RE. Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking. Structure 2019; 27:1326-1335.e4. [PMID: 31257108 DOI: 10.1016/j.str.2019.05.012] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 03/14/2019] [Accepted: 05/30/2019] [Indexed: 12/19/2022]
Abstract
Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.
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Affiliation(s)
- Jeffrey R Wagner
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Christopher P Churas
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Shuai Liu
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Robert V Swift
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Michael Chiu
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Chenghua Shao
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Victoria A Feher
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Stephen K Burley
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Michael K Gilson
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA.
| | - Rommie E Amaro
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA; Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA.
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8
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Sydow D, Morger A, Driller M, Volkamer A. TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data. J Cheminform 2019; 11:29. [PMID: 30963287 PMCID: PMC6454689 DOI: 10.1186/s13321-019-0351-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 03/27/2019] [Indexed: 11/25/2022] Open
Abstract
Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCADD .
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Affiliation(s)
- Dominique Sydow
- In Silico Toxicology, Institute of Physiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Andrea Morger
- In Silico Toxicology, Institute of Physiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Maximilian Driller
- In Silico Toxicology, Institute of Physiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Andrea Volkamer
- In Silico Toxicology, Institute of Physiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
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9
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Riniker S, Landrum GA, Montanari F, Villalba SD, Maier J, Jansen JM, Walters WP, Shelat AA. Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria. F1000Res 2017; 6:1136. [PMID: 28928948 DOI: 10.12688/f1000research.11905.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/11/2017] [Indexed: 12/21/2022] Open
Abstract
The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.
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Affiliation(s)
- Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland
| | | | - Floriane Montanari
- Pharmacoinformatics Research Group, Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Santiago D Villalba
- IMP - Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria
| | - Julie Maier
- Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Johanna M Jansen
- Department of Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Emeryville, CA, USA
| | | | - Anang A Shelat
- Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA
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10
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Riniker S, Landrum GA, Montanari F, Villalba SD, Maier J, Jansen JM, Walters WP, Shelat AA. Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria. F1000Res 2017. [PMID: 28928948 PMCID: PMC5580409 DOI: 10.12688/f1000research.11905.2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.
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Affiliation(s)
- Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland
| | | | - Floriane Montanari
- Pharmacoinformatics Research Group, Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Santiago D Villalba
- IMP - Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria
| | - Julie Maier
- Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Johanna M Jansen
- Department of Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Emeryville, CA, USA
| | | | - Anang A Shelat
- Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA
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11
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Smith RD, Damm-Ganamet KL, Dunbar JB, Ahmed A, Chinnaswamy K, Delproposto JE, Kubish GM, Tinberg CE, Khare SD, Dou J, Doyle L, Stuckey JA, Baker D, Carlson HA. CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge. J Chem Inf Model 2015; 56:1022-31. [PMID: 26419257 DOI: 10.1021/acs.jcim.5b00387] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Community Structure-Activity Resource (CSAR) conducted a benchmark exercise to evaluate the current computational methods for protein design, ligand docking, and scoring/ranking. The exercise consisted of three phases. The first phase required the participants to identify and rank order which designed sequences were able to bind the small molecule digoxigenin. The second phase challenged the community to select a near-native pose of digoxigenin from a set of decoy poses for two of the designed proteins. The third phase investigated the ability of current methods to rank/score the binding affinity of 10 related steroids to one of the designed proteins (pKd = 4.1 to 6.7). We found that 11 of 13 groups were able to correctly select the sequence that bound digoxigenin, with most groups providing the correct three-dimensional structure for the backbone of the protein as well as all atoms of the active-site residues. Eleven of the 14 groups were able to select the appropriate pose from a set of plausible decoy poses. The ability to predict absolute binding affinities is still a difficult task, as 8 of 14 groups were able to correlate scores to affinity (Pearson-r > 0.7) of the designed protein for congeneric steroids and only 5 of 14 groups were able to correlate the ranks of the 10 related ligands (Spearman-ρ > 0.7).
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Affiliation(s)
- Richard D Smith
- Department of Medicinal Chemistry, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
| | - Kelly L Damm-Ganamet
- Department of Medicinal Chemistry, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
| | - James B Dunbar
- Department of Medicinal Chemistry, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
| | - Aqeel Ahmed
- Department of Medicinal Chemistry, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
| | - Krishnapriya Chinnaswamy
- Life Sciences Institute, University of Michigan , 210 Washtenaw Ave, Ann Arbor, Michigan 48109-2216, United States
| | - James E Delproposto
- Life Sciences Institute, University of Michigan , 210 Washtenaw Ave, Ann Arbor, Michigan 48109-2216, United States
| | - Ginger M Kubish
- Life Sciences Institute, University of Michigan , 210 Washtenaw Ave, Ann Arbor, Michigan 48109-2216, United States
| | | | | | | | - Lindsey Doyle
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center , 1100 Fairview Ave. N., Seattle, Washington 98109, United States
| | - Jeanne A Stuckey
- Life Sciences Institute, University of Michigan , 210 Washtenaw Ave, Ann Arbor, Michigan 48109-2216, United States
| | | | - Heather A Carlson
- Department of Medicinal Chemistry, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
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